(1) Field of the Invention
The present invention relates to the field of monitoring the operation of a device. The present invention relates in particular to a method of merging health indicators of a device and to means for implementing said method.
The present invention is more particularly designed for monitoring the operation of a rotary wing aircraft and of various devices included therein. By way of example, the method of the invention applies to monitoring a mechanical power transmission mechanism inserted between at least one fuel-burning engine and at least one rotor of said aircraft, or indeed to monitoring a fuel-burning engine.
(2) Description of Related Art
Monitoring and detecting the presence of a fault within a device and also of the risk of such a fault appearing is the topic of a large amount of research.
In particular, the monitoring systems referred to as Health and Usage Monitoring System(s) (HUMS) aim to monitor one or more generally dynamic devices by means of various sensors tracking variations in a set of health indicators. These health indicators are activated on the basis of measurements from one or more sensors so as to characterize the state and the operation of each device. By way of example, a health indicator may be defined by a signal combining the signals from a plurality of sensors. A plurality of health indicators may also involve the measurements from a single sensor by using various different characteristics of the signal provided by said sensor, such as its frequency spectrum or also a time-varying signal. By way of example, a health indicator is constituted by the maximum amplitude of a time-varying signal provided by an accelerometer.
The variation of each health indicator is compared respectively to a fault detection threshold corresponding to said health indicator so as to detect the possible presence of a fault or a failure of the monitored device or indeed so as to detect a risk of such a fault or of such a failure appearing. The value for each fault detection threshold may be determined by experiment, by statistical analysis on the operation of a fleet of a particular type of aircraft with a given health and usage monitoring system (HUMS), or else by individual training on a given aircraft.
Such a fault detection threshold is generally a minimum threshold or a maximum threshold. If it is a maximum threshold, then so long as the health indicator remains below said maximum threshold, no presence or risk of a fault or a failure appearing is detected. However, when the health indicator is greater than or equal to said maximum threshold, then the presence or a risk of a fault or a failure appearing is detected. Conversely, if it is a minimum threshold, then so long as the health indicator remains above said minimum threshold, no presence or risk of a fault or a failure appearing is detected. However, when the health indicator is less than or equal to said minimum threshold, then the presence or a risk of a fault appearing or of a failure appearing is detected.
In the description below, it is assumed for simplification purposes that a fault detection threshold is a maximum threshold.
This fault detection threshold may be a constant value, but it could also vary depending on various parameters, which parameters possibly include at least one other health indicator of the monitored device.
This fault detection threshold may also be a double threshold made up of a lower limit and an upper limit, thus constituting a range. The presence of a fault is thus generally detected when the health indicator is found outside the range.
In addition, a fault detection threshold may also vary as a function of time during the operation of the monitored device.
By way of example, a fault detection threshold may be calculated by using a history of the operation of the monitored device, said history being determined during a training period while the device is healthy, and such a threshold then being referred to as a “learned threshold”. During said training period, the fault detection threshold is equal to a predetermined constant value, the learned threshold subsequently being bounded by said predetermined constant value.
A fault detection threshold may also be a moving threshold, which is determined over a first time period and which is used over a second time period, said moving threshold thus potentially varying over the entire duration of operation of the monitored device.
By way of example, document US2005/0096873 describes a method of diagnosing a mechanical system using vibration analysis. That method processes vibration signals over a wide band of frequencies coming from various sensors. Those signals are then compared with signals including known faults so as to determine the current state of the mechanical system. In addition, the vibratory signals may be combined with operational information such as component temperatures or indeed the frequency of rotation of a motion transmission shaft, for example.
Document FR 2 972 025 is also known, which describes a method of predicting maintenance operations on an aircraft engine. That method uses a database containing feedback about the causes of the failures of such engines and about the levels of intervention required in order to repair them. That database also contains data about said engines such as the type of engine, and its degree of aging, for example. A statistical failure model modeling the causes of failure and adapted to each type of engine may thus be defined and characterized by a curve plotting the cumulative probability of failure as a function of time. That curve is based on Weibull's distribution law, and decision rules concerning levels of intervention are associated with that failure model as a function of parameters relating to each engine.
In addition, document US2009/0281735 describes a method of determining a future time for performing a maintenance operation on a component or on a subsystem. That future time is defined using a state indicator that is determined at a given instant and using a material value that is determined using a rate of change of that state indicator over a period of time. That future time may be defined by a number of cycles to be carried out before performing a maintenance operation.
In addition, document US2011/0173496 describes a diagnostic method during which a database is defined using history data of selected variables for one or more components. Then, specific characteristics are calculated using said database and hypotheses about the operating states of said components are determined by evaluating said specific characteristics. Finally, an existing state of each component is defined for each hypothesis and possible preventative maintenance operations are deduced.
In addition, the prior art in the field of monitoring operation of a device includes the following documents US2008/0082299, EP 2 204 778, U.S. Pat. No. 6,564,119, and US2014/0149325.
Such monitoring systems thus make it possible to keep the deterioration processes of mechanical assemblies under control by monitoring their operating states in real time so as to anticipate and detect possible faults or failures. Implementing such a monitoring system makes it possible to keep the risks of a technical failure appearing during flight under control and to postpone or to anticipate maintenance tasks. In this way, maintenance costs can be reduced and equipment availability for the client can be increased.
However, the health indicators need to be numerous, of the order of several hundreds, in order to monitor in effective and reliable manner an entire vehicle such as a rotary wing aircraft. Thus, it may be complex for an operator to track those health indicators.
In order to facilitate the work of the operator and in order to improve its effectiveness, a plurality of health indicators may be merged in order to form a merged health indicator, referenced “MHI”.
Several methods for merging health indicators have been developed. By way of example, health indicators associated with a particular component or with a particular mechanical subsystem are grouped together into an indicator group. Each indicator group thus makes it possible to calculate a merged health indicator MHI. This merged health indicator MHI is then compared with an alarm threshold and thus makes it possible to detect an abnormal behavior of said component or of said mechanical substation. By way of example, such a mechanical subsystem may be an engine, or a mechanical or an electrical power transmission mechanism.
Such merging methods provide two main advantages in the field of monitoring the operation of a device. Firstly, the number of health indicators tracked by an operator is reduced, e.g. from several hundreds of health indicators to a few tens of merged health indicators MHI for an entire vehicle. Furthermore, detection performance is improved because additional information is obtained by correlation between a plurality of health indicators, where such additional information cannot be obtained by analyzing health indicators individually and independently.
In general, such merging methods use a training period during which health indicator values are collected while the monitored element is healthy and is operating correctly. A cloud of operating points represented by the health indicator values may then be modeled in a health indicator space in order to represent the healthy state of said element and thus make it possible to determine a healthy-state model for said element. A merged health indicator MHI may be defined in order to represent the position of any operating point in said health indicator space relative to said model for said element. Said merged health indicator MHI may, for example, be characterized by a distance between said operating point and the center of said model for said element. Such a merged health indicator MHI may then be compared with an alarm threshold Sa equal to the distance between said center and the boundary of said model representing said healthy state of said element.
By way of example, document US 2008/0208487 describes a method of estimating the lifetime remaining for a component for a subsystem by merging a plurality of models for determining said remaining lifetime. That method makes it possible to combine the results of those methods with redundant information from those models in order to improve the accuracy and reliability of the estimated lifetime remaining for the component or the subsystem.
In spite of the above-mentioned improvements, such merging methods alone do not make it possible to guarantee that they will detect all of the events that would be detected by a traditional monitoring method in which the health indicators are compared independently and individually with their respective thresholds. Indeed, the healthy-state model for said element as determined by such a merging method may cover a zone in the health indicator space that goes beyond the fault detection threshold(s) for one or more health indicators, thus failing to set off an alarm when said fault detection threshold is crossed.
It is then necessary to impose additional constraints on the merging method in order to remove those risks of non-detection, but those additional constraints may then generate a high rate of false alarms.
It is also possible to combine a comparison between the merged health indicator MHI and its alarm threshold with a comparison between one or more health indicators and their respective fault detection thresholds. However, such a technique is thus more complex than comparing each health indicator individually with its respective fault detection threshold. In addition, such a technique presents several disadvantages, in particular firstly possible inconsistencies between the alarms based on the health indicators and the alarms based on the merged health indicator MHI and secondly the need for continuing to monitor the health indicators individually.